#' Count occurrences by grade

#'

#' @description `r lifecycle::badge("stable")`

#'

#' The analyze function [count_occurrences_by_grade()] creates a layout element to calculate occurrence counts by grade.

#'

#' This function analyzes primary analysis variable `var` which indicates toxicity grades. The `id` variable

#' is used to indicate unique subject identifiers (defaults to `USUBJID`). The user can also supply a list of

#' custom groups of grades to analyze via the `grade_groups` parameter. The `remove_single`  argument will

#' remove single grades from the analysis so that *only* grade groups are analyzed.

#'

#' If there are multiple grades recorded for one patient only the highest grade level is counted.

#'

#' The summarize function [summarize_occurrences_by_grade()] performs the same function as

#' [count_occurrences_by_grade()] except it creates content rows, not data rows, to summarize the current table

#' row/column context and operates on the level of the latest row split or the root of the table if no row splits have

#' occurred.

#'

#' @inheritParams count_occurrences

#' @inheritParams argument_convention

#' @param grade_groups (named `list` of `character`)\cr list containing groupings of grades.

#' @param remove_single (`flag`)\cr `TRUE` to not include the elements of one-element grade groups

#'   in the the output list; in this case only the grade groups names will be included in the output. If

#'   `only_grade_groups` is set to `TRUE` this argument is ignored.

#' @param only_grade_groups (`flag`)\cr whether only the specified grade groups should be

#'   included, with individual grade rows removed (`TRUE`), or all grades and grade groups

#'   should be displayed (`FALSE`).

#' @param .stats (`character`)\cr statistics to select for the table.

#'

#'   Options are: ``r shQuote(get_stats("count_occurrences_by_grade"), type = "sh")``

#'

#' @seealso Relevant helper function [h_append_grade_groups()].

#'

#' @name count_occurrences_by_grade

#' @order 1

NULL

#' Helper function for `s_count_occurrences_by_grade()`

#'

#' @description `r lifecycle::badge("stable")`

#'

#' Helper function for [s_count_occurrences_by_grade()] to insert grade groupings into list with

#' individual grade frequencies. The order of the final result follows the order of `grade_groups`.

#' The elements under any-grade group (if any), i.e. the grade group equal to `refs` will be moved to

#' the end. Grade groups names must be unique.

#'

#' @inheritParams count_occurrences_by_grade

#' @param refs (named `list` of `numeric`)\cr named list where each name corresponds to a reference grade level

#'   and each entry represents a count.

#'

#' @return Formatted list of grade groupings.

#'

#' @examples

#' h_append_grade_groups(

#'   list(

#'     "Any Grade" = as.character(1:5),

#'     "Grade 1-2" = c("1", "2"),

#'     "Grade 3-4" = c("3", "4")

#'   ),

#'   list("1" = 10, "2" = 20, "3" = 30, "4" = 40, "5" = 50)

#' )

#'

#' h_append_grade_groups(

#'   list(

#'     "Any Grade" = as.character(5:1),

#'     "Grade A" = "5",

#'     "Grade B" = c("4", "3")

#'   ),

#'   list("1" = 10, "2" = 20, "3" = 30, "4" = 40, "5" = 50)

#' )

#'

#' h_append_grade_groups(

#'   list(

#'     "Any Grade" = as.character(1:5),

#'     "Grade 1-2" = c("1", "2"),

#'     "Grade 3-4" = c("3", "4")

#'   ),

#'   list("1" = 10, "2" = 5, "3" = 0)

#' )

#'

#' @export

h_append_grade_groups <- function(grade_groups, refs, remove_single = TRUE, only_grade_groups = FALSE) {

  ### compute sums in groups

  }

  ### order result while keeping grade_groups's ordering

  # elements of any-grade group (if any) will be moved to the end

  # groups-elements combined sequence

  # append remaining elements (if any)

  # remove elements of single-element groups, if any

  }

  # apply the order

  # remove groups without any elements in the original refs

  # note: it's OK if groups have 0 value

}

#' @describeIn count_occurrences_by_grade Statistics function which counts the

#'  number of patients by highest grade.

#'

#' @return

#' * `s_count_occurrences_by_grade()` returns a list of counts and fractions with one element per grade level or

#'   grade level grouping.

#'

#' @examples

#' s_count_occurrences_by_grade(

#'   df,

#'   .N_col = 10L,

#'   .var = "AETOXGR",

#'   id = "USUBJID",

#'   grade_groups = list("ANY" = levels(df$AETOXGR))

#' )

#'

#' @export

s_count_occurrences_by_grade <- function(df,

                                         .var,

                                         .N_row, # nolint

                                         .N_col, # nolint

                                         id = "USUBJID",

                                         grade_groups = list(),

                                         remove_single = TRUE,

                                         only_grade_groups = FALSE,

                                         denom = c("N_col", "n", "N_row"),

                                         labelstr = "") {

    )

  } else {

    } else {

    }

      } else {

      }

    }

      )

    }

  }

  }

      } else {

      }

    },

  )

  )

}

#' @describeIn count_occurrences_by_grade Formatted analysis function which is used as `afun`

#'   in `count_occurrences_by_grade()`.

#'

#' @return

#' * `a_count_occurrences_by_grade()` returns the corresponding list with formatted [rtables::CellValue()].

#'

#' @examples

#' a_count_occurrences_by_grade(

#'   df,

#'   .N_col = 10L,

#'   .N_row = 10L,

#'   .var = "AETOXGR",

#'   id = "USUBJID",

#'   grade_groups = list("ANY" = levels(df$AETOXGR))

#' )

#'

#' @export

a_count_occurrences_by_grade <- function(df,

                                         labelstr = "",

                                         id = "USUBJID",

                                         grade_groups = list(),

                                         remove_single = TRUE,

                                         only_grade_groups = FALSE,

                                         denom = c("N_col", "n", "N_row"),

                                         .N_col, # nolint

                                         .N_row, # nolint

                                         .df_row,

                                         .var = NULL,

                                         .stats = NULL,

                                         .formats = NULL,

                                         .labels = NULL,

                                         .indent_mods = NULL,

                                         na_str = default_na_str()) {

  )

  }

  # Fill in with formatting defaults if needed

  # Unlist stats

  # Auto format handling

  )

}

#' @describeIn count_occurrences_by_grade Layout-creating function which can take statistics function

#'   arguments and additional format arguments. This function is a wrapper for [rtables::analyze()].

#'

#' @return

#' * `count_occurrences_by_grade()` returns a layout object suitable for passing to further layouting functions,

#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing

#'   the statistics from `s_count_occurrences_by_grade()` to the table layout.

#'

#' @examples

#' library(dplyr)

#'

#' df <- data.frame(

#'   USUBJID = as.character(c(1:6, 1)),

#'   ARM = factor(c("A", "A", "A", "B", "B", "B", "A"), levels = c("A", "B")),

#'   AETOXGR = factor(c(1, 2, 3, 4, 1, 2, 3), levels = c(1:5)),

#'   AESEV = factor(

#'     x = c("MILD", "MODERATE", "SEVERE", "MILD", "MILD", "MODERATE", "SEVERE"),

#'     levels = c("MILD", "MODERATE", "SEVERE")

#'   ),

#'   stringsAsFactors = FALSE

#' )

#'

#' df_adsl <- df %>%

#'   select(USUBJID, ARM) %>%

#'   unique()

#'

#' # Layout creating function with custom format.

#' basic_table() %>%

#'   split_cols_by("ARM") %>%

#'   add_colcounts() %>%

#'   count_occurrences_by_grade(

#'     var = "AESEV",

#'     .formats = c("count_fraction" = "xx.xx (xx.xx%)")

#'   ) %>%

#'   build_table(df, alt_counts_df = df_adsl)

#'

#' # Define additional grade groupings.

#' grade_groups <- list(

#'   "-Any-" = c("1", "2", "3", "4", "5"),

#'   "Grade 1-2" = c("1", "2"),

#'   "Grade 3-5" = c("3", "4", "5")

#' )

#'

#' basic_table() %>%

#'   split_cols_by("ARM") %>%

#'   add_colcounts() %>%

#'   count_occurrences_by_grade(

#'     var = "AETOXGR",

#'     grade_groups = grade_groups,

#'     only_grade_groups = TRUE

#'   ) %>%

#'   build_table(df, alt_counts_df = df_adsl)

#'

#' @export

#' @order 2

count_occurrences_by_grade <- function(lyt,

                                       var,

                                       id = "USUBJID",

                                       grade_groups = list(),

                                       remove_single = TRUE,

                                       only_grade_groups = FALSE,

                                       var_labels = var,

                                       show_labels = "default",

                                       riskdiff = FALSE,

                                       na_str = default_na_str(),

                                       nested = TRUE,

                                       ...,

                                       table_names = var,

                                       .stats = "count_fraction",

                                       .formats = list(count_fraction = format_count_fraction_fixed_dp),

                                       .indent_mods = NULL,

                                       .labels = NULL) {

  )

  )

  } else {

      )

    )

  }

  )

}

#' @describeIn count_occurrences_by_grade Layout-creating function which can take content function arguments

#'   and additional format arguments. This function is a wrapper for [rtables::summarize_row_groups()].

#'

#' @return

#' * `summarize_occurrences_by_grade()` returns a layout object suitable for passing to further layouting functions,

#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted content rows

#'   containing the statistics from `s_count_occurrences_by_grade()` to the table layout.

#'

#' @examples

#' # Layout creating function with custom format.

#' basic_table() %>%

#'   add_colcounts() %>%

#'   split_rows_by("ARM", child_labels = "visible", nested = TRUE) %>%

#'   summarize_occurrences_by_grade(

#'     var = "AESEV",

#'     .formats = c("count_fraction" = "xx.xx (xx.xx%)")

#'   ) %>%

#'   build_table(df, alt_counts_df = df_adsl)

#'

#' basic_table() %>%

#'   add_colcounts() %>%

#'   split_rows_by("ARM", child_labels = "visible", nested = TRUE) %>%

#'   summarize_occurrences_by_grade(

#'     var = "AETOXGR",

#'     grade_groups = grade_groups

#'   ) %>%

#'   build_table(df, alt_counts_df = df_adsl)

#'

#' @export

#' @order 3

summarize_occurrences_by_grade <- function(lyt,

                                           var,

                                           id = "USUBJID",

                                           grade_groups = list(),

                                           remove_single = TRUE,

                                           only_grade_groups = FALSE,

                                           riskdiff = FALSE,

                                           na_str = default_na_str(),

                                           ...,

                                           .stats = "count_fraction",

                                           .formats = list(count_fraction = format_count_fraction_fixed_dp),

                                           .indent_mods = NULL,

                                           .labels = NULL) {

  )

  )

  } else {

      )

    )

  }

  )

}

#' Control functions for Kaplan-Meier plot annotation tables

#'

#' @description `r lifecycle::badge("stable")`

#'

#' Auxiliary functions for controlling arguments for formatting the annotation tables that can be added to plots

#' generated via [g_km()].

#'

#' @param x (`proportion`)\cr x-coordinate for center of annotation table.

#' @param y (`proportion`)\cr y-coordinate for center of annotation table.

#' @param w (`proportion`)\cr relative width of the annotation table.

#' @param h (`proportion`)\cr relative height of the annotation table.

#' @param fill (`flag` or `character`)\cr whether the annotation table should have a background fill color.

#'   Can also be a color code to use as the background fill color. If `TRUE`, color code defaults to `"#00000020"`.

#'

#' @return A list of components with the same names as the arguments.

#'

#' @seealso [g_km()]

#'

#' @name control_annot

NULL

#' @describeIn control_annot Control function for formatting the median survival time annotation table. This annotation

#'   table can be added in [g_km()] by setting `annot_surv_med=TRUE`, and can be configured using the

#'   `control_surv_med_annot()` function by setting it as the `control_annot_surv_med` argument.

#'

#' @examples

#' control_surv_med_annot()

#'

#' @export

control_surv_med_annot <- function(x = 0.8, y = 0.85, w = 0.32, h = 0.16, fill = TRUE) {

}

#' @describeIn control_annot Control function for formatting the Cox-PH annotation table. This annotation table can be

#'   added in [g_km()] by setting `annot_coxph=TRUE`, and can be configured using the `control_coxph_annot()` function

#'   by setting it as the `control_annot_coxph` argument.

#'

#' @param ref_lbls (`flag`)\cr whether the reference group should be explicitly printed in labels for the

#'   annotation table. If `FALSE` (default), only comparison groups will be printed in the table labels.

#'

#' @examples

#' control_coxph_annot()

#'

#' @export

control_coxph_annot <- function(x = 0.29, y = 0.51, w = 0.4, h = 0.125, fill = TRUE, ref_lbls = FALSE) {

}

#' Helper function to calculate x-tick positions

#'

#' @description `r lifecycle::badge("stable")`

#'

#' Calculate the positions of ticks on the x-axis. However, if `xticks` already

#' exists it is kept as is. It is based on the same function `ggplot2` relies on,

#' and is required in the graphic and the patient-at-risk annotation table.

#'

#' @inheritParams g_km

#' @inheritParams h_ggkm

#'

#' @return A vector of positions to use for x-axis ticks on a `ggplot` object.

#'

#' @examples

#' library(dplyr)

#' library(survival)

#'

#' data <- tern_ex_adtte %>%

#'   filter(PARAMCD == "OS") %>%

#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%

#'   h_data_plot()

#'

#' h_xticks(data)

#' h_xticks(data, xticks = seq(0, 3000, 500))

#' h_xticks(data, xticks = 500)

#' h_xticks(data, xticks = 500, max_time = 6000)

#' h_xticks(data, xticks = c(0, 500), max_time = 300)

#' h_xticks(data, xticks = 500, max_time = 300)

#'

#' @export

h_xticks <- function(data, xticks = NULL, max_time = NULL) {

    } else {

    }

    } else {

    }

  } else {

      )

    )

  }

}

#' Helper function for survival estimations

#'

#' @description `r lifecycle::badge("stable")`

#'

#' Transform a survival fit to a table with groups in rows characterized by N, median and confidence interval.

#'

#' @inheritParams h_data_plot

#'

#' @return A summary table with statistics `N`, `Median`, and `XX% CI` (`XX` taken from `fit_km`).

#'

#' @examples

#' library(dplyr)

#' library(survival)

#'

#' adtte <- tern_ex_adtte %>% filter(PARAMCD == "OS")

#' fit <- survfit(

#'   formula = Surv(AVAL, 1 - CNSR) ~ ARMCD,

#'   data = adtte

#' )

#' h_tbl_median_surv(fit_km = fit)

#'

#' @export

h_tbl_median_surv <- function(fit_km, armval = "All") {

  } else {

  }

  )

  )

}

#' Helper function for generating a pairwise Cox-PH table

#'

#' @description `r lifecycle::badge("stable")`

#'

#' Create a `data.frame` of pairwise stratified or unstratified Cox-PH analysis results.

#'

#' @inheritParams g_km

#' @param annot_coxph_ref_lbls (`flag`)\cr whether the reference group should be explicitly printed in labels for the

#'   `annot_coxph` table. If `FALSE` (default), only comparison groups will be printed in `annot_coxph` table labels.

#'

#' @return A `data.frame` containing statistics `HR`, `XX% CI` (`XX` taken from `control_coxph_pw`),

#'   and `p-value (log-rank)`.

#'

#' @examples

#' library(dplyr)

#'

#' adtte <- tern_ex_adtte %>%

#'   filter(PARAMCD == "OS") %>%

#'   mutate(is_event = CNSR == 0)

#'

#' h_tbl_coxph_pairwise(

#'   df = adtte,

#'   variables = list(tte = "AVAL", is_event = "is_event", arm = "ARM"),

#'   control_coxph_pw = control_coxph(conf_level = 0.9)

#' )

#'

#' @export

h_tbl_coxph_pairwise <- function(df,

                                 variables,

                                 ref_group_coxph = NULL,

                                 control_coxph_pw = control_coxph(),

                                 annot_coxph_ref_lbls = FALSE) {

    )

  }

    )

      ),

    )

}

#' Helper function to tidy survival fit data

#'

#' @description `r lifecycle::badge("stable")`

#'

#' Convert the survival fit data into a data frame designed for plotting

#' within `g_km`.

#'

#' This starts from the [broom::tidy()] result, and then:

#'   * Post-processes the `strata` column into a factor.

#'   * Extends each stratum by an additional first row with time 0 and probability 1 so that

#'     downstream plot lines start at those coordinates.

#'   * Adds a `censor` column.

#'   * Filters the rows before `max_time`.

#'

#' @inheritParams g_km

#' @param fit_km (`survfit`)\cr result of [survival::survfit()].

#' @param armval (`string`)\cr used as strata name when treatment arm variable only has one level. Default is `"All"`.

#'

#' @return A `tibble` with columns `time`, `n.risk`, `n.event`, `n.censor`, `estimate`, `std.error`, `conf.high`,

#'   `conf.low`, `strata`, and `censor`.

#'

#' @examples

#' library(dplyr)

#' library(survival)

#'

#' # Test with multiple arms

#' tern_ex_adtte %>%

#'   filter(PARAMCD == "OS") %>%

#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%

#'   h_data_plot()

#'

#' # Test with single arm

#' tern_ex_adtte %>%

#'   filter(PARAMCD == "OS", ARMCD == "ARM B") %>%

#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%

#'   h_data_plot(armval = "ARM B")

#'

#' @export

h_data_plot <- function(fit_km,

                        armval = "All",

                        max_time = NULL) {

    )

  } else {

  }

      )

    }

  )

  }

}

## Deprecated Functions ----

#' Helper function to create a KM plot

#'

#' @description `r lifecycle::badge("deprecated")`

#'

#' Draw the Kaplan-Meier plot using `ggplot2`.

#'

#' @inheritParams g_km

#' @param data (`data.frame`)\cr survival data as pre-processed by `h_data_plot`.

#'

#' @return A `ggplot` object.

#'

#' @examples

#' \donttest{

#' library(dplyr)

#' library(survival)

#'

#' fit_km <- tern_ex_adtte %>%

#'   filter(PARAMCD == "OS") %>%

#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)

#' data_plot <- h_data_plot(fit_km = fit_km)

#' xticks <- h_xticks(data = data_plot)

#' gg <- h_ggkm(

#'   data = data_plot,

#'   censor_show = TRUE,

#'   xticks = xticks,

#'   xlab = "Days",

#'   yval = "Survival",

#'   ylab = "Survival Probability",

#'   title = "Survival"

#' )

#' gg

#' }

#'

#' @export

h_ggkm <- function(data,

                   xticks = NULL,

                   yval = "Survival",

                   censor_show,

                   xlab,

                   ylab,

                   ylim = NULL,

                   title,

                   footnotes = NULL,

                   max_time = NULL,

                   lwd = 1,

                   lty = NULL,

                   pch = 3,

                   size = 2,

                   col = NULL,

                   ci_ribbon = FALSE,

                   ggtheme = nestcolor::theme_nest()) {

  )

    } else {

    }

  }

  # change estimates of survival to estimates of failure (1 - survival)

  }

      )

    ) +

  }

  }

  }

  }

      ),

    ) +

      )

  }

    } else {

    }

  }

  }

  )

}

#' `ggplot` decomposition

#'

#' @description `r lifecycle::badge("deprecated")`

#'

#' The elements composing the `ggplot` are extracted and organized in a `list`.

#'

#' @param gg (`ggplot`)\cr a graphic to decompose.

#'

#' @return A named `list` with elements:

#'   * `panel`: The panel.

#'   * `yaxis`: The y-axis.

#'   * `xaxis`: The x-axis.

#'   * `xlab`: The x-axis label.

#'   * `ylab`: The y-axis label.

#'   * `guide`: The legend.

#'

#' @examples

#' \donttest{

#' library(dplyr)

#' library(survival)

#' library(grid)

#'

#' fit_km <- tern_ex_adtte %>%

#'   filter(PARAMCD == "OS") %>%

#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)

#' data_plot <- h_data_plot(fit_km = fit_km)

#' xticks <- h_xticks(data = data_plot)

#' gg <- h_ggkm(

#'   data = data_plot,

#'   yval = "Survival",

#'   censor_show = TRUE,

#'   xticks = xticks, xlab = "Days", ylab = "Survival Probability",

#'   title = "tt",

#'   footnotes = "ff"

#' )

#'

#' g_el <- h_decompose_gg(gg)

#' grid::grid.newpage()

#' grid.rect(gp = grid::gpar(lty = 1, col = "red", fill = "gray85", lwd = 5))

#' grid::grid.draw(g_el$panel)

#'

#' grid::grid.newpage()

#' grid.rect(gp = grid::gpar(lty = 1, col = "royalblue", fill = "gray85", lwd = 5))

#' grid::grid.draw(with(g_el, cbind(ylab, yaxis)))

#' }

#'

#' @export

h_decompose_gg <- function(gg) {

  )

  )

}

#' Helper function to prepare a KM layout

#'

#' @description `r lifecycle::badge("deprecated")`

#'

#' Prepares a (5 rows) x (2 cols) layout for the Kaplan-Meier curve.

#'

#' @inheritParams g_km

#' @inheritParams h_ggkm

#' @param g_el (`list` of `gtable`)\cr list as obtained by `h_decompose_gg()`.

#' @param annot_at_risk (`flag`)\cr compute and add the annotation table reporting the number of

#'   patient at risk matching the main grid of the Kaplan-Meier curve.

#'

#' @return A grid layout.

#'

#' @details The layout corresponds to a grid of two columns and five rows of unequal dimensions. Most of the

#'   dimension are fixed, only the curve is flexible and will accommodate with the remaining free space.

#'   * The left column gets the annotation of the `ggplot` (y-axis) and the names of the strata for the patient

#'     at risk tabulation. The main constraint is about the width of the columns which must allow the writing of

#'     the strata name.

#'   * The right column receive the `ggplot`, the legend, the x-axis and the patient at risk table.

#'

#' @examples

#' \donttest{

#' library(dplyr)

#' library(survival)

#' library(grid)

#'

#' fit_km <- tern_ex_adtte %>%

#'   filter(PARAMCD == "OS") %>%

#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)

#' data_plot <- h_data_plot(fit_km = fit_km)

#' xticks <- h_xticks(data = data_plot)

#' gg <- h_ggkm(

#'   data = data_plot,

#'   censor_show = TRUE,

#'   xticks = xticks, xlab = "Days", ylab = "Survival Probability",

#'   title = "tt", footnotes = "ff", yval = "Survival"

#' )

#' g_el <- h_decompose_gg(gg)

#' lyt <- h_km_layout(data = data_plot, g_el = g_el, title = "t", footnotes = "f")

#' grid.show.layout(lyt)

#' }

#'

#' @export

h_km_layout <- function(data, g_el, title, footnotes, annot_at_risk = TRUE, annot_at_risk_title = TRUE) {

  )

        )

      )

    )

  )

  }

  )

  )

  }

    }

  } else {

  }

    )

  )

}

#' Helper function to create patient-at-risk grobs

#'

#' @description `r lifecycle::badge("deprecated")`

#'

#' Two graphical objects are obtained, one corresponding to row labeling and the second to the table of

#' numbers of patients at risk. If `title = TRUE`, a third object corresponding to the table title is

#' also obtained.

#'

#' @inheritParams g_km

#' @inheritParams h_ggkm

#' @param annot_tbl (`data.frame`)\cr annotation as prepared by [survival::summary.survfit()] which

#'   includes the number of patients at risk at given time points.

#' @param xlim (`numeric(1)`)\cr the maximum value on the x-axis (used to ensure the at risk table aligns with the KM

#'   graph).

#' @param title (`flag`)\cr whether the "Patients at Risk" title should be added above the `annot_at_risk`

#'   table. Has no effect if `annot_at_risk` is `FALSE`. Defaults to `TRUE`.

#'

#' @return A named `list` of two `gTree` objects if `title = FALSE`: `at_risk` and `label`, or three

#'   `gTree` objects if `title = TRUE`: `at_risk`, `label`, and `title`.

#'

#' @examples

#' \donttest{

#' library(dplyr)

#' library(survival)

#' library(grid)

#'

#' fit_km <- tern_ex_adtte %>%

#'   filter(PARAMCD == "OS") %>%

#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)

#'

#' data_plot <- h_data_plot(fit_km = fit_km)

#'

#' xticks <- h_xticks(data = data_plot)

#'

#' gg <- h_ggkm(

#'   data = data_plot,

#'   censor_show = TRUE,

#'   xticks = xticks, xlab = "Days", ylab = "Survival Probability",

#'   title = "tt", footnotes = "ff", yval = "Survival"

#' )

#'

#' # The annotation table reports the patient at risk for a given strata and

#' # times (`xticks`).

#' annot_tbl <- summary(fit_km, times = xticks)

#' if (is.null(fit_km$strata)) {

#'   annot_tbl <- with(annot_tbl, data.frame(n.risk = n.risk, time = time, strata = "All"))

#' } else {

#'   strata_lst <- strsplit(sub("=", "equals", levels(annot_tbl$strata)), "equals")

#'   levels(annot_tbl$strata) <- matrix(unlist(strata_lst), ncol = 2, byrow = TRUE)[, 2]

#'   annot_tbl <- data.frame(

#'     n.risk = annot_tbl$n.risk,

#'     time = annot_tbl$time,

#'     strata = annot_tbl$strata

#'   )

#' }

#'

#' # The annotation table is transformed into a grob.

#' tbl <- h_grob_tbl_at_risk(data = data_plot, annot_tbl = annot_tbl, xlim = max(xticks))

#'

#' # For the representation, the layout is estimated for which the decomposition

#' # of the graphic element is necessary.

#' g_el <- h_decompose_gg(gg)

#' lyt <- h_km_layout(data = data_plot, g_el = g_el, title = "t", footnotes = "f")

#'

#' grid::grid.newpage()

#' pushViewport(viewport(layout = lyt, height = .95, width = .95))

#' grid.rect(gp = grid::gpar(lty = 1, col = "purple", fill = "gray85", lwd = 1))

#' pushViewport(viewport(layout.pos.row = 3:4, layout.pos.col = 2))

#' grid.rect(gp = grid::gpar(lty = 1, col = "orange", fill = "gray85", lwd = 1))

#' grid::grid.draw(tbl$at_risk)

#' popViewport()

#' pushViewport(viewport(layout.pos.row = 3:4, layout.pos.col = 1))

#' grid.rect(gp = grid::gpar(lty = 1, col = "green3", fill = "gray85", lwd = 1))

#' grid::grid.draw(tbl$label)

#' }

#'

#' @export

h_grob_tbl_at_risk <- function(data, annot_tbl, xlim, title = TRUE) {

  )

      )

    )

  }

    ),

      ),

    )

  )

    ),

    )

  )

            ),

          )

        )

      )

    ),

            ),

          )

        )

      )

    )

  )

            ),

          )

        )

      )

    )

  }

}

#' Helper function to create survival estimation grobs

#'

#' @description `r lifecycle::badge("deprecated")`

#'

#' The survival fit is transformed in a grob containing a table with groups in

#' rows characterized by N, median and 95% confidence interval.

#'

#' @inheritParams g_km

#' @inheritParams h_data_plot

#' @param ttheme (`list`)\cr see [gridExtra::ttheme_default()].

#' @param x (`proportion`)\cr a value between 0 and 1 specifying x-location.

#' @param y (`proportion`)\cr a value between 0 and 1 specifying y-location.

#' @param width (`grid::unit`)\cr width (as a unit) to use when printing the grob.

#'

#' @return A `grob` of a table containing statistics `N`, `Median`, and `XX% CI` (`XX` taken from `fit_km`).

#'

#' @examples

#' \donttest{

#' library(dplyr)

#' library(survival)

#' library(grid)

#'

#' grid::grid.newpage()

#' grid.rect(gp = grid::gpar(lty = 1, col = "pink", fill = "gray85", lwd = 1))

#' tern_ex_adtte %>%

#'   filter(PARAMCD == "OS") %>%

#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%

#'   h_grob_median_surv() %>%

#'   grid::grid.draw()

#' }

#'

#' @export

h_grob_median_surv <- function(fit_km,

                               armval = "All",

                               x = 0.9,

                               y = 0.9,

                               width = grid::unit(0.3, "npc"),

                               ttheme = gridExtra::ttheme_default()) {

  )

  ))

  })

  })

  }

  )

  )

    )

  )

}

#' Helper function to create grid object with y-axis annotation

#'

#' @description `r lifecycle::badge("deprecated")`

#'

#' Build the y-axis annotation from a decomposed `ggplot`.

#'

#' @param ylab (`gtable`)\cr the y-lab as a graphical object derived from a `ggplot`.

#' @param yaxis (`gtable`)\cr the y-axis as a graphical object derived from a `ggplot`.

#'

#' @return A `gTree` object containing the y-axis annotation from a `ggplot`.

#'

#' @examples

#' \donttest{

#' library(dplyr)

#' library(survival)

#' library(grid)

#'

#' fit_km <- tern_ex_adtte %>%

#'   filter(PARAMCD == "OS") %>%

#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)

#' data_plot <- h_data_plot(fit_km = fit_km)

#' xticks <- h_xticks(data = data_plot)

#' gg <- h_ggkm(

#'   data = data_plot,

#'   censor_show = TRUE,

#'   xticks = xticks, xlab = "Days", ylab = "Survival Probability",

#'   title = "title", footnotes = "footnotes", yval = "Survival"

#' )

#'

#' g_el <- h_decompose_gg(gg)

#'

#' grid::grid.newpage()

#' pvp <- grid::plotViewport(margins = c(5, 4, 2, 20))

#' pushViewport(pvp)

#' grid::grid.draw(h_grob_y_annot(ylab = g_el$ylab, yaxis = g_el$yaxis))

#' grid.rect(gp = grid::gpar(lty = 1, col = "gray35", fill = NA))

#' }

#'

#' @export

h_grob_y_annot <- function(ylab, yaxis) {

  )

      ),

    )

  )

}

#' Helper function to create Cox-PH grobs

#'

#' @description `r lifecycle::badge("deprecated")`

#'

#' Grob of `rtable` output from [h_tbl_coxph_pairwise()]

#'

#' @inheritParams h_grob_median_surv

#' @param ... arguments to pass to [h_tbl_coxph_pairwise()].

#' @param x (`proportion`)\cr a value between 0 and 1 specifying x-location.

#' @param y (`proportion`)\cr a value between 0 and 1 specifying y-location.

#' @param width (`grid::unit`)\cr width (as a unit) to use when printing the grob.

#'

#' @return A `grob` of a table containing statistics `HR`, `XX% CI` (`XX` taken from `control_coxph_pw`),

#'   and `p-value (log-rank)`.

#'

#' @examples

#' \donttest{

#' library(dplyr)

#' library(survival)